A contrastive learning framework for safety information extraction in construction

Jiajing Liu , Hanbin Luo , Weili Fang , Peter E.D. Love

Abstract

Typically named entity recognition (NER) and relation extraction (RE) from safety documentation (e.g., accident reports) adopt a pipeline processing approach whereby tasks are split into two sub-tasks. As a result, error propagation occurs between components, and useful information from one task may go unexploited by the other. Additionally, training sets to perform NER and RE from safety documentation are often limited and context-specific. Thus, our research addresses the following question: How can we accurately identify entities and extract relations from safety documentation using limited training sets? This paper utilizes ‘contrastive learning’ to tackle our research question. It proposes a contrastive learning-based cascade binary tagging framework (CasRel) to automatically and synchronously extract entities and relations from safety documents. A five-fold cross-validation process is used to validate the effectiveness and feasibility of our approach. Results from the validation process achieve an average precision of 77.8%, recall of 58.7%, and F1-score of 66.9%, outperforming CasRel with an increase of about 10% in precision, 5% in recall, and 7% in F1-score. Thus, our approach can accurately recognize entities and extract relations from safety documentation. The contributions of our study are twofold: (1) an improved unified model is developed to recognize and extract the entity and relation from safety documents to reduce error propagation and improve its accuracy; and (2) the concept of ‘contrastive learning’ is introduced in the design of the joint entity and relation extraction model with limited training sets.

https://www.sciencedirect.com/science/article/pii/S1474034623003221